Measurement of the multineutron ν ¯ μ charged current differential cross section at low available energy on hydrocarbon
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Engineering topics
Publications and source records attributed to Jena, D..
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Using low-temperature cathodoluminescence spectroscopy, we study the properties of N- and Al-polar AlN layers grown by molecular beam epitaxy on bulk AlN{0001}. Compared with the bulk AlN substrate, layers of both polarities feature a suppression of deep-level luminescence, a total absence of the prevalent donor with an exciton binding energy of 28 meV, and a much increased intensity of the emission from free excitons. The dominant donor in these layers is characterized by an associated exciton binding energy of 13 meV. The observation of excited exciton states up to the exciton continuum allows us to directly extract the Γ 5 free exciton binding energy of 57 meV.
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Scattering of high energy particles from nucleons probes their structure, as was done in the experiments that established the non-zero size of the proton using electron beams. The use of charged leptons as scattering probes enables measuring the distribution of electric charges, which is encoded in the vector form factors of the nucleon. Scattering weakly interacting neutrinos gives the opportunity to measure both vector and axial vector form factors of the nucleon, providing an additional, complementary probe of their structure. The nucleon transition axial form factor, F A , can be measured from neutrino scattering from free nucleons, ν μ n → μ – p and ν¯ μ p → μ + n , as a function of the negative four-momentum transfer squared (Q 2 ). Up to now, FA(Q 2 ) has been extracted from the bound nucleons in neutrino–deuterium scattering, which requires uncertain nuclear corrections. Here we report the first high-statistics measurement, to our knowledge, of the ν¯ μ p → μ + n cross-section from the hydrogen atom, using the plastic scintillator target of the MINERvA experiment, extracting F A from free proton targets and measuring the nucleon axial charge radius, r A , to be 0.73 ± 0.17 fm. The antineutrino–hydrogen scattering presented here can access the axial form factor without the need for nuclear theory corrections, and enables direct comparisons with the increasingly precise lattice quantum chromodynamics computations. Finally, the tools developed for this analysis and the result presented are substantial advancements in our capabilities to understand the nucleon structure in the weak sector, and also help the current and future neutrino oscillation experiments to better constrain neutrino interaction models.
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We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.
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We present a unified thermionic emission (TE) and thermionic-field emission (TFE) model for the ideal reverse-bias leakage current in Schottky junctions. The unified TE–TFE analytical model advances upon previous analytical TFE models by Murphy–Good and Padovani–Stratton, which are the two most widely adopted models by the community, in two major aspects: (i) the applicability of the TFE expression therein is extended to near-zero surface electric fields by an error-function correction, allowing for the calculation of the total current by a nontrivial sum of TE (over-the-barrier current) and TFE (below-the-barrier current) contributions; therefore, an accurate description of the TE-to-TFE transition region is captured analytically for the first time; (ii) image-force lowering is considered with much-simpler correction terms. Comparisons with the reference numerical model show that the unified TE–TFE model has excellent accuracy, as well as a 10 000× reduction in computation time. The unified model is further tested against experimental data from Schottky barrier diodes based on Si, 4H-SiC, GaN, and β-Ga 2 O 3 , revealing accurate extractions of barrier heights and correct descriptions of the ideal reverse leakage characteristics. With the extended applicable range, improved accuracy, and computational efficiency, the unified TE–TFE model is highly valuable for the design and analysis of devices based on Schottky junctions, as well as for potential integration in technology computer-aided design (TCAD) tools.
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With the advance of particle accelerator and detector technologies, the neutrino physics landscape is rapidly expanding. As neutrino oscillation experiments enter the intensity and precision frontiers, neutrino–nucleus interaction measurements are providing crucial input. MINERvA is an experiment at Fermilab dedicated to the study of neutrino–nucleus interactions in the regime of incident neutrino energies from one to few GeV. The experiment recorded neutrino and antineutrino scattering data with the NuMI beamline from 2009 to 2019 using the Low-Energy and Medium-Energy beams that peak at 3GeV and 6GeV, respectively. This article reviews the broad spectrum of interesting nuclear and particle physics that MINERvA investigations have illuminated. The newfound, detailed knowledge of neutrino interactions with nuclear targets thereby obtained is proving essential to continued progress in the neutrino physics sector.
Charged-current neutrino interactions with low hadronic recoil ("low-nu") have a cross-section that is approximately constant versus neutrino energy. These interactions have been used to measure the shape of neutrino fluxes as a function of neutrino energy at accelerator-based neutrino experiments such as CCFR, NuTeV, MINOS and MINERvA. In this paper, we demonstrate that low-nu events can be used to measure parameters of neutrino flux and detector models and that utilization of event distributions over the upstream detector face can discriminate among parameters that affect the neutrino flux model. From fitting a large sample of low-nu events obtained by exposing MINERvA to the NuMI medium-energy beam, we find that the best-fit flux parameters are within their a priori uncertainties, but the energy scale of muons reconstructed in the MINOS detector is shifted by 3.6% (or 1.8 times the a priori uncertainty on that parameter). These fit results are now used in all MINERvA cross-section measurements, and this technique can be applied by other experiments operating at MINERvA energies, such as DUNE.
This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013–2017, with an average neutrino energy of 6 GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two γs from 70.7 ± 0.9% to 89.3 ± 0.7% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with 〈E ν〉 between 1–10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current νe events arising from νμ → νe appearance.